In [6]:
plt.figure(figsize=(20,15))
for i,col in enumerate(train_df.columns,1):
plt.subplot(6,4,i)
plt.title(f"Distribution of {col} Data")
sns.histplot(train_df[col],kde=True)
plt.tight_layout()
plt.plot()
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
sns.set(style="darkgrid",font_scale=1.5)
pd.set_option("display.max.rows",None)
pd.set_option("display.max.columns",None)
from lazypredict.Supervised import LazyClassifier
import lazypredict
import plotly.express as px
from sklearn import tree
from sklearn.svm import SVC,LinearSVC
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression,Perceptron,PassiveAggressiveClassifier,RidgeClassifier
from sklearn.semi_supervised import LabelPropagation
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier,NearestCentroid
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, StackingClassifier,VotingClassifier,BaggingRegressor,ExtraTreesClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV
from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score, confusion_matrix , fbeta_score
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler, MinMaxScaler
from imblearn.over_sampling import SMOTE
train_df = pd.read_csv('Parkinsson_disease.csv')
train_df.head()
| name | MDVP:Fo(Hz) | MDVP:Fhi(Hz) | MDVP:Flo(Hz) | MDVP:Jitter(%) | MDVP:Jitter(Abs) | MDVP:RAP | MDVP:PPQ | Jitter:DDP | MDVP:Shimmer | MDVP:Shimmer(dB) | Shimmer:APQ3 | Shimmer:APQ5 | MDVP:APQ | Shimmer:DDA | NHR | HNR | status | RPDE | DFA | spread1 | spread2 | D2 | PPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | phon_R01_S01_1 | 119.99 | 157.30 | 75.00 | 0.01 | 0.00 | 0.00 | 0.01 | 0.01 | 0.04 | 0.43 | 0.02 | 0.03 | 0.03 | 0.07 | 0.02 | 21.03 | 1 | 0.41 | 0.82 | -4.81 | 0.27 | 2.30 | 0.28 |
| 1 | phon_R01_S01_2 | 122.40 | 148.65 | 113.82 | 0.01 | 0.00 | 0.00 | 0.01 | 0.01 | 0.06 | 0.63 | 0.03 | 0.05 | 0.04 | 0.09 | 0.02 | 19.09 | 1 | 0.46 | 0.82 | -4.08 | 0.34 | 2.49 | 0.37 |
| 2 | phon_R01_S01_3 | 116.68 | 131.11 | 111.56 | 0.01 | 0.00 | 0.01 | 0.01 | 0.02 | 0.05 | 0.48 | 0.03 | 0.04 | 0.04 | 0.08 | 0.01 | 20.65 | 1 | 0.43 | 0.83 | -4.44 | 0.31 | 2.34 | 0.33 |
| 3 | phon_R01_S01_4 | 116.68 | 137.87 | 111.37 | 0.01 | 0.00 | 0.01 | 0.01 | 0.02 | 0.05 | 0.52 | 0.03 | 0.04 | 0.04 | 0.09 | 0.01 | 20.64 | 1 | 0.43 | 0.82 | -4.12 | 0.33 | 2.41 | 0.37 |
| 4 | phon_R01_S01_5 | 116.01 | 141.78 | 110.66 | 0.01 | 0.00 | 0.01 | 0.01 | 0.02 | 0.06 | 0.58 | 0.03 | 0.05 | 0.04 | 0.10 | 0.02 | 19.65 | 1 | 0.42 | 0.82 | -3.75 | 0.23 | 2.33 | 0.41 |
train_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 195 entries, 0 to 194 Data columns (total 24 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 name 195 non-null object 1 MDVP:Fo(Hz) 195 non-null float64 2 MDVP:Fhi(Hz) 195 non-null float64 3 MDVP:Flo(Hz) 195 non-null float64 4 MDVP:Jitter(%) 195 non-null float64 5 MDVP:Jitter(Abs) 195 non-null float64 6 MDVP:RAP 195 non-null float64 7 MDVP:PPQ 195 non-null float64 8 Jitter:DDP 195 non-null float64 9 MDVP:Shimmer 195 non-null float64 10 MDVP:Shimmer(dB) 195 non-null float64 11 Shimmer:APQ3 195 non-null float64 12 Shimmer:APQ5 195 non-null float64 13 MDVP:APQ 195 non-null float64 14 Shimmer:DDA 195 non-null float64 15 NHR 195 non-null float64 16 HNR 195 non-null float64 17 status 195 non-null int64 18 RPDE 195 non-null float64 19 DFA 195 non-null float64 20 spread1 195 non-null float64 21 spread2 195 non-null float64 22 D2 195 non-null float64 23 PPE 195 non-null float64 dtypes: float64(22), int64(1), object(1) memory usage: 36.7+ KB
train_df.isnull().values.any()
False
train_df.drop("name",axis=1,inplace=True)
train_df.drop_duplicates(inplace=True)
plt.figure(figsize=(20,15))
for i,col in enumerate(train_df.columns,1):
plt.subplot(6,4,i)
plt.title(f"Distribution of {col} Data")
sns.histplot(train_df[col],kde=True)
plt.tight_layout()
plt.plot()
numeric_columns = (list(train_df.loc[:]))
X = train_df.drop(columns=["status"])
y = train_df[["status"]]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
x_train,x_test,y_train,y_test = train_test_split(X,y,stratify=y,test_size=0.3,random_state=0)
x_train1, x_test1, y_train1, y_test1 = train_test_split(X_scaled,y,stratify=y,test_size=0.3,random_state=0)
training_score = []
testing_score = []
ff1_score = []
ffbeta_score = []
def model_prediction(model):
model.fit(x_train1,y_train1)
x_train_pred1 = model.predict(x_train1)
x_test_pred1 = model.predict(x_test1)
a = accuracy_score(y_train1,x_train_pred1)
b = accuracy_score(y_test1,x_test_pred1)
c = f1_score(y_test1,x_test_pred1)
d = fbeta_score(y_test1,x_test_pred1,beta=2.0)
training_score.append(a)
testing_score.append(b)
ff1_score.append(c)
ffbeta_score.append(d)
print(f"Accuracy_Score of {model} model on Training Data is:",a)
print(f"Accuracy_Score of {model} model on Testing Data is:",b)
print("\n------------------------------------------------------------------------")
print(f"Precision Score of {model} model is:",precision_score(y_test1,x_test_pred1))
print(f"Recall Score of {model} model is:",recall_score(y_test1,x_test_pred1))
print(f"F1 Score of {model} model is:",f1_score(y_test1,x_test_pred1))
print("\n------------------------------------------------------------------------")
print(f"Confusion Matrix of {model} model is:")
cm = confusion_matrix(y_test1,x_test_pred1)
plt.figure(figsize=(8,4))
sns.heatmap(cm,annot=True,fmt="g",cmap="Blues")
plt.show()
model_prediction(LogisticRegression())
Accuracy_Score of LogisticRegression() model on Training Data is: 0.875 Accuracy_Score of LogisticRegression() model on Testing Data is: 0.7796610169491526 ------------------------------------------------------------------------ Precision Score of LogisticRegression() model is: 0.8974358974358975 Recall Score of LogisticRegression() model is: 0.7954545454545454 F1 Score of LogisticRegression() model is: 0.8433734939759037 ------------------------------------------------------------------------ Confusion Matrix of LogisticRegression() model is:
model_prediction(KNeighborsClassifier())
Accuracy_Score of KNeighborsClassifier() model on Training Data is: 0.9264705882352942 Accuracy_Score of KNeighborsClassifier() model on Testing Data is: 0.8813559322033898 ------------------------------------------------------------------------ Precision Score of KNeighborsClassifier() model is: 0.9302325581395349 Recall Score of KNeighborsClassifier() model is: 0.9090909090909091 F1 Score of KNeighborsClassifier() model is: 0.9195402298850575 ------------------------------------------------------------------------ Confusion Matrix of KNeighborsClassifier() model is:
model_prediction(SVC())
Accuracy_Score of SVC() model on Training Data is: 0.8897058823529411 Accuracy_Score of SVC() model on Testing Data is: 0.8983050847457628 ------------------------------------------------------------------------ Precision Score of SVC() model is: 0.8958333333333334 Recall Score of SVC() model is: 0.9772727272727273 F1 Score of SVC() model is: 0.9347826086956522 ------------------------------------------------------------------------ Confusion Matrix of SVC() model is:
model_prediction(MLPClassifier())
Accuracy_Score of MLPClassifier() model on Training Data is: 0.9485294117647058 Accuracy_Score of MLPClassifier() model on Testing Data is: 0.864406779661017 ------------------------------------------------------------------------ Precision Score of MLPClassifier() model is: 0.9285714285714286 Recall Score of MLPClassifier() model is: 0.8863636363636364 F1 Score of MLPClassifier() model is: 0.9069767441860465 ------------------------------------------------------------------------ Confusion Matrix of MLPClassifier() model is:
model_prediction(Perceptron())
Accuracy_Score of Perceptron() model on Training Data is: 0.8823529411764706 Accuracy_Score of Perceptron() model on Testing Data is: 0.6949152542372882 ------------------------------------------------------------------------ Precision Score of Perceptron() model is: 0.90625 Recall Score of Perceptron() model is: 0.6590909090909091 F1 Score of Perceptron() model is: 0.763157894736842 ------------------------------------------------------------------------ Confusion Matrix of Perceptron() model is:
model_prediction(PassiveAggressiveClassifier())
Accuracy_Score of PassiveAggressiveClassifier() model on Training Data is: 0.8529411764705882 Accuracy_Score of PassiveAggressiveClassifier() model on Testing Data is: 0.9152542372881356 ------------------------------------------------------------------------ Precision Score of PassiveAggressiveClassifier() model is: 0.8979591836734694 Recall Score of PassiveAggressiveClassifier() model is: 1.0 F1 Score of PassiveAggressiveClassifier() model is: 0.9462365591397849 ------------------------------------------------------------------------ Confusion Matrix of PassiveAggressiveClassifier() model is:
model_prediction(RidgeClassifier())
Accuracy_Score of RidgeClassifier() model on Training Data is: 0.8897058823529411 Accuracy_Score of RidgeClassifier() model on Testing Data is: 0.8305084745762712 ------------------------------------------------------------------------ Precision Score of RidgeClassifier() model is: 0.9047619047619048 Recall Score of RidgeClassifier() model is: 0.8636363636363636 F1 Score of RidgeClassifier() model is: 0.8837209302325582 ------------------------------------------------------------------------ Confusion Matrix of RidgeClassifier() model is:
model_prediction(ExtraTreesClassifier())
Accuracy_Score of ExtraTreesClassifier() model on Training Data is: 1.0 Accuracy_Score of ExtraTreesClassifier() model on Testing Data is: 0.8813559322033898 ------------------------------------------------------------------------ Precision Score of ExtraTreesClassifier() model is: 0.8936170212765957 Recall Score of ExtraTreesClassifier() model is: 0.9545454545454546 F1 Score of ExtraTreesClassifier() model is: 0.9230769230769231 ------------------------------------------------------------------------ Confusion Matrix of ExtraTreesClassifier() model is:
model_prediction(NearestCentroid())
Accuracy_Score of NearestCentroid() model on Training Data is: 0.7647058823529411 Accuracy_Score of NearestCentroid() model on Testing Data is: 0.6610169491525424 ------------------------------------------------------------------------ Precision Score of NearestCentroid() model is: 0.9 Recall Score of NearestCentroid() model is: 0.6136363636363636 F1 Score of NearestCentroid() model is: 0.7297297297297297 ------------------------------------------------------------------------ Confusion Matrix of NearestCentroid() model is:
model_prediction(GaussianNB())
Accuracy_Score of GaussianNB() model on Training Data is: 0.75 Accuracy_Score of GaussianNB() model on Testing Data is: 0.6440677966101694 ------------------------------------------------------------------------ Precision Score of GaussianNB() model is: 0.896551724137931 Recall Score of GaussianNB() model is: 0.5909090909090909 F1 Score of GaussianNB() model is: 0.7123287671232877 ------------------------------------------------------------------------ Confusion Matrix of GaussianNB() model is:
def model_prediction(model):
model.fit(x_train,y_train)
x_train_pred = model.predict(x_train)
x_test_pred = model.predict(x_test)
a = accuracy_score(y_train,x_train_pred)
b = accuracy_score(y_test,x_test_pred)
c = f1_score(y_test,x_test_pred)
d = fbeta_score(y_test,x_test_pred,beta=2.0)
training_score.append(a)
testing_score.append(b)
ff1_score.append(c)
ffbeta_score.append(d)
print(f"Accuracy_Score of {model} model on Training Data is:",a)
print(f"Accuracy_Score of {model} model on Testing Data is:",b)
print("\n------------------------------------------------------------------------")
print(f"Precision Score of {model} model is:",precision_score(y_test,x_test_pred))
print(f"Recall Score of {model} model is:",recall_score(y_test,x_test_pred))
print(f"F1 Score of {model} model is:",f1_score(y_test,x_test_pred))
print("\n------------------------------------------------------------------------")
print(f"Confusion Matrix of {model} model is:")
cm = confusion_matrix(y_test,x_test_pred)
plt.figure(figsize=(8,4))
sns.heatmap(cm,annot=True,fmt="g",cmap="Blues")
plt.show()
model_prediction(DecisionTreeClassifier())
Accuracy_Score of DecisionTreeClassifier() model on Training Data is: 1.0 Accuracy_Score of DecisionTreeClassifier() model on Testing Data is: 0.864406779661017 ------------------------------------------------------------------------ Precision Score of DecisionTreeClassifier() model is: 0.9285714285714286 Recall Score of DecisionTreeClassifier() model is: 0.8863636363636364 F1 Score of DecisionTreeClassifier() model is: 0.9069767441860465 ------------------------------------------------------------------------ Confusion Matrix of DecisionTreeClassifier() model is:
model_prediction(RandomForestClassifier())
Accuracy_Score of RandomForestClassifier() model on Training Data is: 1.0 Accuracy_Score of RandomForestClassifier() model on Testing Data is: 0.864406779661017 ------------------------------------------------------------------------ Precision Score of RandomForestClassifier() model is: 0.9090909090909091 Recall Score of RandomForestClassifier() model is: 0.9090909090909091 F1 Score of RandomForestClassifier() model is: 0.9090909090909091 ------------------------------------------------------------------------ Confusion Matrix of RandomForestClassifier() model is:
model_prediction(AdaBoostClassifier())
Accuracy_Score of AdaBoostClassifier() model on Training Data is: 1.0 Accuracy_Score of AdaBoostClassifier() model on Testing Data is: 0.8983050847457628 ------------------------------------------------------------------------ Precision Score of AdaBoostClassifier() model is: 0.9130434782608695 Recall Score of AdaBoostClassifier() model is: 0.9545454545454546 F1 Score of AdaBoostClassifier() model is: 0.9333333333333332 ------------------------------------------------------------------------ Confusion Matrix of AdaBoostClassifier() model is:
model_prediction(GradientBoostingClassifier())
Accuracy_Score of GradientBoostingClassifier() model on Training Data is: 1.0 Accuracy_Score of GradientBoostingClassifier() model on Testing Data is: 0.8813559322033898 ------------------------------------------------------------------------ Precision Score of GradientBoostingClassifier() model is: 0.8936170212765957 Recall Score of GradientBoostingClassifier() model is: 0.9545454545454546 F1 Score of GradientBoostingClassifier() model is: 0.9230769230769231 ------------------------------------------------------------------------ Confusion Matrix of GradientBoostingClassifier() model is:
model_prediction(XGBClassifier(learning_rate=0.01, n_estimators=25, max_depth=15,gamma=0.6, subsample=0.52,colsample_bytree=0.6,seed=27,
reg_lambda=2, booster='dart', colsample_bylevel=0.6, colsample_bynode=0.5))
Accuracy_Score of XGBClassifier(base_score=None, booster='dart', callbacks=None,
colsample_bylevel=0.6, colsample_bynode=0.5, colsample_bytree=0.6,
early_stopping_rounds=None, enable_categorical=False,
eval_metric=None, feature_types=None, gamma=0.6, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=0.01, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=15, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=25, n_jobs=None, num_parallel_tree=None,
predictor=None, random_state=None, ...) model on Training Data is: 0.8970588235294118
Accuracy_Score of XGBClassifier(base_score=None, booster='dart', callbacks=None,
colsample_bylevel=0.6, colsample_bynode=0.5, colsample_bytree=0.6,
early_stopping_rounds=None, enable_categorical=False,
eval_metric=None, feature_types=None, gamma=0.6, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=0.01, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=15, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=25, n_jobs=None, num_parallel_tree=None,
predictor=None, random_state=None, ...) model on Testing Data is: 0.7627118644067796
------------------------------------------------------------------------
Precision Score of XGBClassifier(base_score=None, booster='dart', callbacks=None,
colsample_bylevel=0.6, colsample_bynode=0.5, colsample_bytree=0.6,
early_stopping_rounds=None, enable_categorical=False,
eval_metric=None, feature_types=None, gamma=0.6, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=0.01, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=15, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=25, n_jobs=None, num_parallel_tree=None,
predictor=None, random_state=None, ...) model is: 0.8571428571428571
Recall Score of XGBClassifier(base_score=None, booster='dart', callbacks=None,
colsample_bylevel=0.6, colsample_bynode=0.5, colsample_bytree=0.6,
early_stopping_rounds=None, enable_categorical=False,
eval_metric=None, feature_types=None, gamma=0.6, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=0.01, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=15, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=25, n_jobs=None, num_parallel_tree=None,
predictor=None, random_state=None, ...) model is: 0.8181818181818182
F1 Score of XGBClassifier(base_score=None, booster='dart', callbacks=None,
colsample_bylevel=0.6, colsample_bynode=0.5, colsample_bytree=0.6,
early_stopping_rounds=None, enable_categorical=False,
eval_metric=None, feature_types=None, gamma=0.6, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=0.01, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=15, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=25, n_jobs=None, num_parallel_tree=None,
predictor=None, random_state=None, ...) model is: 0.8372093023255814
------------------------------------------------------------------------
Confusion Matrix of XGBClassifier(base_score=None, booster='dart', callbacks=None,
colsample_bylevel=0.6, colsample_bynode=0.5, colsample_bytree=0.6,
early_stopping_rounds=None, enable_categorical=False,
eval_metric=None, feature_types=None, gamma=0.6, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=0.01, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=15, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=25, n_jobs=None, num_parallel_tree=None,
predictor=None, random_state=None, ...) model is:
model1 = SVC()
model1.fit(x_train,y_train)
SVC()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
SVC()
model2 = KNeighborsClassifier()
model2.fit(x_train,y_train)
KNeighborsClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
KNeighborsClassifier()
model3 = XGBClassifier()
parameters3 = {"n_estimators":[50,100,150],
"random_state":[0,42,50],
"learning_rate":[0.1,0.3,0.5,1.0]}
grid_search3 = GridSearchCV(model3, parameters3 , cv=5, n_jobs=-1)
grid_search3.fit(x_train,y_train)
GridSearchCV(cv=5,
estimator=XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False, eval_metric=None,
feature_types=None, gamma=None,
gpu_id=None, grow_policy=None,
importance_type=None,
interaction_constraints=None,
learning_rate=None,...
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=None, ...),
n_jobs=-1,
param_grid={'learning_rate': [0.1, 0.3, 0.5, 1.0],
'n_estimators': [50, 100, 150],
'random_state': [0, 42, 50]})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=5,
estimator=XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False, eval_metric=None,
feature_types=None, gamma=None,
gpu_id=None, grow_policy=None,
importance_type=None,
interaction_constraints=None,
learning_rate=None,...
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=None, ...),
n_jobs=-1,
param_grid={'learning_rate': [0.1, 0.3, 0.5, 1.0],
'n_estimators': [50, 100, 150],
'random_state': [0, 42, 50]})XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=None, num_parallel_tree=None,
predictor=None, random_state=None, ...)XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=None, num_parallel_tree=None,
predictor=None, random_state=None, ...)best_parameters3 = grid_search3.best_params_
best_parameters3
{'learning_rate': 0.3, 'n_estimators': 150, 'random_state': 0}
model3 = XGBClassifier(**best_parameters3)
model3.fit(x_train,y_train)
XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=0.3, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None, num_parallel_tree=None,
predictor=None, random_state=0, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=0.3, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None, num_parallel_tree=None,
predictor=None, random_state=0, ...)fig, ax = plt.subplots(figsize=(30, 30))
from xgboost import plot_tree
plot_tree(model3,num_trees=0,rankdir="LR",ax=ax)
plt.show()
model4 = DecisionTreeClassifier()
model4.fit(x_train,y_train)
DecisionTreeClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
DecisionTreeClassifier()
clf = tree.DecisionTreeClassifier()
clf = clf.fit(x_train,y_train)
model5 = AdaBoostClassifier()
model5.fit(x_train,y_train)
AdaBoostClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
AdaBoostClassifier()
model6 = GradientBoostingClassifier()
model6.fit(x_train,y_train)
GradientBoostingClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
GradientBoostingClassifier()
from deslib.des import DESP
from deslib.des import KNORAE
from deslib.des import KNORAU
from deslib.des import KNOP
pool_classifiers = [model1, model2, model3, model4, model5, model6]
desp = DESP(pool_classifiers)
knorau = KNORAU(pool_classifiers)
kne = KNORAE(pool_classifiers)
Desp = model_prediction(desp)
Accuracy_Score of DESP(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False, eval_metric=None,
feature_types=None, gamma=None,
gpu_id=None, grow_policy=None,
importance_type=None,
interaction_constraints=...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model on Training Data is: 1.0
Accuracy_Score of DESP(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False, eval_metric=None,
feature_types=None, gamma=None,
gpu_id=None, grow_policy=None,
importance_type=None,
interaction_constraints=...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model on Testing Data is: 0.9322033898305084
------------------------------------------------------------------------
Precision Score of DESP(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False, eval_metric=None,
feature_types=None, gamma=None,
gpu_id=None, grow_policy=None,
importance_type=None,
interaction_constraints=...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model is: 0.9166666666666666
Recall Score of DESP(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False, eval_metric=None,
feature_types=None, gamma=None,
gpu_id=None, grow_policy=None,
importance_type=None,
interaction_constraints=...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model is: 1.0
F1 Score of DESP(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False, eval_metric=None,
feature_types=None, gamma=None,
gpu_id=None, grow_policy=None,
importance_type=None,
interaction_constraints=...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model is: 0.9565217391304348
------------------------------------------------------------------------
Confusion Matrix of DESP(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False, eval_metric=None,
feature_types=None, gamma=None,
gpu_id=None, grow_policy=None,
importance_type=None,
interaction_constraints=...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model is:
Knorau = model_prediction(knorau)
Accuracy_Score of KNORAU(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False,
eval_metric=None, feature_types=None,
gamma=None, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraint...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model on Training Data is: 1.0
Accuracy_Score of KNORAU(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False,
eval_metric=None, feature_types=None,
gamma=None, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraint...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model on Testing Data is: 0.9322033898305084
------------------------------------------------------------------------
Precision Score of KNORAU(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False,
eval_metric=None, feature_types=None,
gamma=None, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraint...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model is: 0.9166666666666666
Recall Score of KNORAU(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False,
eval_metric=None, feature_types=None,
gamma=None, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraint...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model is: 1.0
F1 Score of KNORAU(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False,
eval_metric=None, feature_types=None,
gamma=None, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraint...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model is: 0.9565217391304348
------------------------------------------------------------------------
Confusion Matrix of KNORAU(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False,
eval_metric=None, feature_types=None,
gamma=None, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraint...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model is:
Kne = model_prediction(kne)
Accuracy_Score of KNORAE(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False,
eval_metric=None, feature_types=None,
gamma=None, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraint...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model on Training Data is: 1.0
Accuracy_Score of KNORAE(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False,
eval_metric=None, feature_types=None,
gamma=None, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraint...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model on Testing Data is: 0.9152542372881356
------------------------------------------------------------------------
Precision Score of KNORAE(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False,
eval_metric=None, feature_types=None,
gamma=None, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraint...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model is: 0.9148936170212766
Recall Score of KNORAE(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False,
eval_metric=None, feature_types=None,
gamma=None, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraint...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model is: 0.9772727272727273
F1 Score of KNORAE(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False,
eval_metric=None, feature_types=None,
gamma=None, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraint...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model is: 0.945054945054945
------------------------------------------------------------------------
Confusion Matrix of KNORAE(pool_classifiers=[SVC(), KNeighborsClassifier(),
XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None,
early_stopping_rounds=None,
enable_categorical=False,
eval_metric=None, feature_types=None,
gamma=None, gpu_id=None,
grow_policy=None, importance_type=None,
interaction_constraint...
learning_rate=0.3, max_bin=None,
max_cat_threshold=None,
max_cat_to_onehot=None,
max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=150, n_jobs=None,
num_parallel_tree=None, predictor=None,
random_state=0, ...),
DecisionTreeClassifier(), AdaBoostClassifier(),
GradientBoostingClassifier()]) model is:
stacking_model2 = StackingClassifier(estimators=[('SVC', model1),
('KNeighborsClassifier', model2),
('GradientBoostingClassifier', model6)])
stacking_model2.fit(x_train, y_train)
StackingClassifier(estimators=[('SVC', SVC()),
('KNeighborsClassifier', KNeighborsClassifier()),
('GradientBoostingClassifier',
GradientBoostingClassifier())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. StackingClassifier(estimators=[('SVC', SVC()),
('KNeighborsClassifier', KNeighborsClassifier()),
('GradientBoostingClassifier',
GradientBoostingClassifier())])SVC()
KNeighborsClassifier()
GradientBoostingClassifier()
LogisticRegression()
x_train_pred5 = stacking_model2.predict(x_train)
x_test_pred5 = stacking_model2.predict(x_test)
print("Stacking Model accuracy on Testing Data is:",accuracy_score(y_test,x_test_pred5)*100)
Stacking Model accuracy on Testing Data is: 91.52542372881356
print("F2 on Testing Data is:",fbeta_score(y_test,x_test_pred5,beta=2.0))
F2 on Testing Data is: 0.9777777777777776
cm = confusion_matrix(y_test,x_test_pred5)
plt.figure(figsize=(8,4))
sns.heatmap(cm,annot=True,fmt="g",cmap="Blues")
plt.show()
a = accuracy_score(y_train,x_train_pred5)
b = accuracy_score(y_test,x_test_pred5)
c = f1_score(y_test,x_test_pred5)
d = fbeta_score(y_test,x_test_pred5,beta=2.0)
training_score.append(a)
testing_score.append(b)
ff1_score.append(c)
ffbeta_score.append(d)
stacking_model3 = VotingClassifier(estimators=[ ('SVC', model1),
('KNeighborsClassifier', model2),
('GradientBoostingClassifier', model6)],voting='hard')
stacking_model3.fit(x_train, y_train)
VotingClassifier(estimators=[('SVC', SVC()),
('KNeighborsClassifier', KNeighborsClassifier()),
('GradientBoostingClassifier',
GradientBoostingClassifier())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. VotingClassifier(estimators=[('SVC', SVC()),
('KNeighborsClassifier', KNeighborsClassifier()),
('GradientBoostingClassifier',
GradientBoostingClassifier())])SVC()
KNeighborsClassifier()
GradientBoostingClassifier()
x_train_pred6 = stacking_model3.predict(x_train)
x_test_pred6 = stacking_model3.predict(x_test)
print("VotingClassifier accuracy on Testing Data is::",accuracy_score(y_test,x_test_pred6)*100)
VotingClassifier accuracy on Testing Data is:: 84.7457627118644
print("VotingClassifier F2 on Testing Data is:",fbeta_score(y_test,x_test_pred6,beta=2.0))
VotingClassifier F2 on Testing Data is: 0.9606986899563318
cm = confusion_matrix(y_test,x_test_pred6)
plt.figure(figsize=(8,4))
sns.heatmap(cm,annot=True,fmt="g",cmap="Blues")
plt.show()
a = accuracy_score(y_train,x_train_pred6)
b = accuracy_score(y_test,x_test_pred6)
c = f1_score(y_test,x_test_pred6)
d = fbeta_score(y_test,x_test_pred6,beta=2.0)
training_score.append(a)
testing_score.append(b)
ff1_score.append(c)
ffbeta_score.append(d)
models = ["Logistic Regression","KNN","SVC","MLPClassifier","Perceptron","PassiveAggressiveClassifier","RidgeClassifier","ExtraTreesClassifier","NearestCentroid","Gausian","Decision Tree","Random Forest","Ada Boost",
"Gradient Boost","XGBoost","Desp","Knorau","Kne","StackingClassifier","VotingClassifier"]
df = pd.DataFrame({"Algorithms":models,
"Training Score":training_score,
"Testing Score (Accuracy)":testing_score,
"F1 Score":ff1_score,
"F2 Score":ffbeta_score,
})
df = df.sort_values(by='F2 Score', ascending=False)
df
| Algorithms | Training Score | Testing Score (Accuracy) | F1 Score | F2 Score | |
|---|---|---|---|---|---|
| 16 | Knorau | 1.00 | 0.93 | 0.96 | 0.98 |
| 15 | Desp | 1.00 | 0.93 | 0.96 | 0.98 |
| 18 | StackingClassifier | 1.00 | 0.92 | 0.95 | 0.98 |
| 5 | PassiveAggressiveClassifier | 0.85 | 0.92 | 0.95 | 0.98 |
| 17 | Kne | 1.00 | 0.92 | 0.95 | 0.96 |
| 19 | VotingClassifier | 0.90 | 0.85 | 0.91 | 0.96 |
| 2 | SVC | 0.89 | 0.90 | 0.93 | 0.96 |
| 12 | Ada Boost | 1.00 | 0.90 | 0.93 | 0.95 |
| 7 | ExtraTreesClassifier | 1.00 | 0.88 | 0.92 | 0.94 |
| 13 | Gradient Boost | 1.00 | 0.88 | 0.92 | 0.94 |
| 1 | KNN | 0.93 | 0.88 | 0.92 | 0.91 |
| 11 | Random Forest | 1.00 | 0.86 | 0.91 | 0.91 |
| 10 | Decision Tree | 1.00 | 0.86 | 0.91 | 0.89 |
| 3 | MLPClassifier | 0.95 | 0.86 | 0.91 | 0.89 |
| 6 | RidgeClassifier | 0.89 | 0.83 | 0.88 | 0.87 |
| 14 | XGBoost | 0.90 | 0.76 | 0.84 | 0.83 |
| 0 | Logistic Regression | 0.88 | 0.78 | 0.84 | 0.81 |
| 4 | Perceptron | 0.88 | 0.69 | 0.76 | 0.70 |
| 8 | NearestCentroid | 0.76 | 0.66 | 0.73 | 0.66 |
| 9 | Gausian | 0.75 | 0.64 | 0.71 | 0.63 |
df.plot(x="Algorithms",y=["F2 Score"], figsize=(16,6),kind="bar",
title="Performance Visualization of Different Models (Ranked by accuracy)",colormap="Set1")
plt.show()
line = px.line(data_frame= df , x="Algorithms", y =["F2 Score","Testing Score (Accuracy)" ] , markers = True)
line.update_xaxes(title="Model",
rangeslider_visible = False)
line.update_yaxes(title = "F2 SCORE")
line.update_layout(showlegend = True,
title = {
'text': 'F2 Score vs Model',
'y':0.94,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})
line.show()
df = df.sort_values(by='Testing Score (Accuracy)', ascending=False)
df.plot(x="Algorithms",y=["Training Score","Testing Score (Accuracy)"], figsize=(16,6),kind="bar",
title="Performance Visualization of Different Models (Ranked by accuracy)",colormap="Set1")
plt.show()
df.plot(x="Algorithms",y=["Testing Score (Accuracy)"], figsize=(16,6),kind="bar",
title="Performance Visualization of Different Models (Ranked by accuracy)",colormap="Set1")
plt.show()
